Identifying a visual stimulus can be substantially impaired by the mere presence of additional stimuli in the immediate vicinity. This phenomenon is called "crowding," and it powerfully limits visual perception in many circumstances, especially in the peripheral visual field. There is a rich body of literature detailing the phenomenology of crowding, but we do not know why crowding occurs. We lack a computational model that can predict what information will be available to an observer in an arbitrary crowded display. A popular hypothesis is that crowding results from obligatory "texture processing," but there have been few efforts to formalize and test what this might mean, despite broad agreement that crowding reflects some form of "excessive integration." Dr. Rosenholtz has extensive experience with computational models of texture processing, which are a powerful means of defining the exact nature of "texture processing" and testing the ability of such models to explain and predict visual behavior. The proposed research has 3 aims: (1) To clarify and formalize the hypothesis that crowding is due to a "texture" - i.e. statistical -- representation of the crowded stimuli. (2) To collect behavioral data from a wider variety of displays and tasks than is typically studied in crowding. (3) To develop and validate the first general-purpose model of visual crowding. To achieve these aims, Dr. Rosenholtz will apply state-of-the-art computational tools for texture synthesis to "crowded" stimuli. "Texturizing" crowded arrays of stimuli affords a tool for visualizing the information available in a crowded display and a vocabulary for describing its representational content. Thus, Dr. Rosenholtz will attack the problem of crowding through a useful synthesis of computer graphics, computer vision, and psychophysics. PUBLIC HEALTH RELEVANCE: Understanding crowding, besides elucidating representations and performance of normal human vision, is crucial for disorders like age-related macular degeneration, for which, without foveal vision, virtually all perception is essentially crowded. In addition, percepts under crowding may be related to percepts under other visual dysfunctions where there is "excessive integration", such as amblyopia and simultagnosia. Successfully predicting crowding severity would also advance the design of low-vision aids for older adults and improve our ability to design for the visually-impaired.